Outlier Detection

Data Science

An interdisciplinary field using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Big Data

Extremely large and complex datasets that require advanced computational tools and techniques to store, process, and analyse.

Data Engineering

The practice of designing, building, and maintaining data infrastructure, pipelines, and architectures.

Exploratory Data Analysis

An approach to analysing datasets to summarise their main characteristics, often using statistical graphics and visualisation.

Statistical Modelling

The process of applying statistical analysis to a dataset, identifying relationships and patterns within the data.

Diagnostic Analytics

Analysis techniques focused on understanding why something happened by examining data patterns and correlations.

Time Series Analysis

Statistical techniques for analysing time-ordered data points to identify trends, cycles, and forecasting patterns.

Regression Analysis

A set of statistical processes for estimating the relationships between dependent and independent variables.

Hypothesis Testing

A statistical method for making decisions about population parameters based on sample data evidence.

Bayesian Statistics

A statistical approach that incorporates prior knowledge and updates probability estimates as new data is observed.

Monte Carlo Simulation

A computational technique using repeated random sampling to obtain numerical results for problems with many coupled variables.

Business Analytics

The practice of iterative exploration of organisational data to drive business planning and decision-making.

An automated set of processes that moves and transforms data from source systems to target destinations.

A controlled experiment methodology that compares two versions of a product, feature, or experience to determine which performs better against a defined metric.

The framework of policies, processes, and standards for managing data assets to ensure quality, security, and compliance.

Using historical data, statistical algorithms, and machine learning to forecast future outcomes and trends.

Artificially generated datasets that preserve the statistical properties of real data while protecting privacy, used for testing, development, and sharing across organisational boundaries.

Processing and analysing continuous data streams in real time to detect patterns and trigger responses.

The graphical representation of data and information using visual elements like charts, graphs, and maps.

The ability for users to ask questions about data in plain language and receive answers, with AI translating natural language into database queries and visualisations.